title: A moment-adjusted imputation method for measurement error models.
abstract: Studies of clinical characteristics frequently measure covariates with a single observation. This may be a mismeasured version of the "true" phenomenon due to sources of variability like biological fluctuations and device error. Descriptive analyses and outcome models that are based on mismeasured data generally will not reflect the corresponding analyses based on the "true" covariate. Many statistical methods are available to adjust for measurement error. Imputation methods like regression calibration and moment reconstruction are easily implemented but are not always adequate. Sophisticated methods have been proposed for specific applications like density estimation, logistic regression, and survival analysis. However, it is frequently infeasible for an analyst to adjust each analysis separately, especially in preliminary studies where resources are limited. We propose an imputation approach called moment-adjusted imputation that is flexible and relatively automatic. Like other imputation methods, it can be used to adjust a variety of analyses quickly, and it performs well under a broad range of circumstances. We illustrate the method via simulation and apply it to a study of systolic blood pressure and health outcomes in patients hospitalized with acute heart failure.
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705, USA. laine.thomas@duke.edu
authors.forename: Laine
authors.initials: L
authors.lastname: Thomas
authors.forename: Leonard
authors.initials: L
authors.lastname: Stefanski
authors.forename: Marie
authors.initials: M
authors.lastname: Davidian
mesh_terms: D000465:Algorithms; D001795:Blood Pressure Determination / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006333:Heart Failure / Q000175:diagnosis* / Q000453:epidemiology*; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity